This project will study neuroimaging, blood, and CSF to measure neurodegeneration associated with AD. Few studies have focused on multiple measures of neurodegeneration in the same subjects by combining informative neuroimaging and peripheral biomarkers to provide a """"""""biosignature,"""""""" in order to improve early diagnosis and treatment monitoring. Neuroimaging studies will include PET scans using probes of amyloid plaques (PIB) and amyloid plaques and tau in tangles (FDDNP), and MRI measures of myelin and white matter tract integrity. Plasma measures of signaling proteins and cerebrospinal fluid (CSF) levels of proteins associated with plaques and tangles (e.g., elevated phosphorylated tau and low Abl-42) and demyelination (e.g., sulfatide) will also be obtained. The UCLA Clinical Core will recruit 80 subjects (40 AD patients and 40 older cognitively-intact controls), and the Center's Imaging and Biomarker Core will assist with data storage and analysis. All subjects will receive neuropsychological testing, scans, and blood tests (apolipoprotein E genotyping and plasma signaling proteins), and an estimated 50 will agree to lumbar punctures for CSF measures. We will test the following hypotheses: (1) Plasma signaling protein biomarkers will differentiate AD patients from controls. (2) CSF Ab and phosphorylated tau will differentiate AD patients from controls. Within the AD and control subject groups, CSF Ab biomarkers will correlate with PIB signals, while both Ab and phosphorylated tau CSF biomarkers will correlate with FDDNP signals. (3) MRI measures of myelin integrity will differentiate AD and control groups. We will also explore possible associations of these MRI measures with CSF sulfatide values. (4) While both FDDNP and PIB signals will differentiate AD from controls, binding patterns will differ. FDDNP will label regions predicted to show tangle as well as plaque deposition;PIB will label predicted plaque-rich regions. Within the AD and control groups, we will also explore correlations between cognitive measures and PET binding signals. We will explore research questions on how well a potential combined-measure or biosignature predicts clinical decline and whether stratifying subjects according to apolipoprotein E genotype influences findings. This project would lay the groundwork for better quantification and understanding of these critical neurodegenerative biomarkers.

Public Health Relevance

Neuroimaging and biomarker information could lead to an informative biosignature that would identify at risk individuals for testing of prevention strategies, identify patients at earlier stages of disease for early intervention strategies, and improve overall diagnostic accuracy, thus facilitating innovative interventions and drug discovery throughout the full longitudinal course of neurodegeneration.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Specialized Center (P50)
Project #
Application #
Study Section
Special Emphasis Panel (ZAG1-ZIJ-4)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of California Los Angeles
Los Angeles
United States
Zip Code
Petyuk, Vladislav A; Chang, Rui; Ramirez-Restrepo, Manuel et al. (2018) The human brainome: network analysis identifies HSPA2 as a novel Alzheimer’s disease target. Brain 141:2721-2739
Burke, Shanna L; Cadet, Tamara; Maddux, Marlaina (2018) Chronic Health Illnesses as Predictors of Mild Cognitive Impairment Among African American Older Adults. J Natl Med Assoc 110:314-325
Cruchaga, Carlos; Del-Aguila, Jorge L; Saef, Benjamin et al. (2018) Polygenic risk score of sporadic late-onset Alzheimer's disease reveals a shared architecture with the familial and early-onset forms. Alzheimers Dement 14:205-214
Joe, Elizabeth; Medina, Luis D; Ringman, John M et al. (2018) 1H MRS spectroscopy in preclinical autosomal dominant Alzheimer disease. Brain Imaging Behav :
Burke, Shanna L; Maramaldi, Peter; Cadet, Tamara et al. (2018) Decreasing hazards of Alzheimer's disease with the use of antidepressants: mitigating the risk of depression and apolipoprotein E. Int J Geriatr Psychiatry 33:200-211
Qian, Winnie; Fischer, Corinne E; Schweizer, Tom A et al. (2018) Association Between Psychosis Phenotype and APOE Genotype on the Clinical Profiles of Alzheimer's Disease. Curr Alzheimer Res 15:187-194
Burke, Shanna L; Hu, Tianyan; Fava, Nicole M et al. (2018) Sex differences in the development of mild cognitive impairment and probable Alzheimer's disease as predicted by hippocampal volume or white matter hyperintensities. J Women Aging :1-25
Wang, Qi; Guo, Lei; Thompson, Paul M et al. (2018) The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1. J Alzheimers Dis 64:149-169
Wang, Tingyan; Qiu, Robin G; Yu, Ming (2018) Predictive Modeling of the Progression of Alzheimer's Disease with Recurrent Neural Networks. Sci Rep 8:9161
Alosco, Michael L; Sugarman, Michael A; Besser, Lilah M et al. (2018) A Clinicopathological Investigation of White Matter Hyperintensities and Alzheimer's Disease Neuropathology. J Alzheimers Dis 63:1347-1360

Showing the most recent 10 out of 727 publications